Evidence map›Paper›PMID 41071935›Full record

ArticleEuropean heart journal. Quality of care & clinical outcomes2026

Using SCORE2 with a risk chart or online calculator: impact on model performance, treatment eligibility, and cardiovascular disease prevention.

Steven H J Hageman, Stephen Kaptoge, Mari N Gynnild, Joris Holtrop, Lisa Pennells, J William McEvoy, Martin Bobak, Andrzej Pająk, Hynek Pikhart, Abdonas Tamosiunas and 8 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Quality of care & clinical outcomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Steven H J HagemanDepartment of Vascular Medicine, University Medical Center Utrecht, PO Box 85500, Utrecht 3508 GA, The Netherlands.ORCID 0000-0003-2299-6745
Stephen KaptogeCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.ORCID 0000-0002-1155-4872
Mari N GynnildK.G. Jebsen Center for Cardiac Biomarkers, Institute of Clinical Medicine, University of Oslo, Norway.
Joris HoltropDepartment of Vascular Medicine, University Medical Center Utrecht, PO Box 85500, Utrecht 3508 GA, The Netherlands.ORCID 0009-0005-7616-3839
Lisa PennellsCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.ORCID 0000-0002-8594-3061
J William McEvoyUniversity of Galway and National Institute for Prevention and Cardiovascular Health, Galway H91 FF68, Ireland.
Martin BobakDepartment of Epidemiology and Public Health, University College London, London WC1E 7HB, UK.ORCID 0000-0002-2633-6851
Andrzej PająkDepartment of Epidemiology and Population Studies, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, 31-066 Kraków, Poland.
Hynek PikhartDepartment of Epidemiology and Public Health, University College London, London WC1E 7HB, UK.ORCID 0000-0001-5277-4049
Abdonas TamosiunasInstitute of Cardiology, Lithuanian University of Health Sciences, LT-44307 Kaunas, Lithuania.ORCID 0000-0002-0868-457X
Yvo M SmuldersInternal Medicine, Amsterdam UMC, Amsterdam 1105 AZ, The Netherlands.
Francois MachDivision of Cardiology, Faculty of Medicine, Geneva University Hospitals, 1205 Geneva, Switzerland.
David CarballoDivision of Cardiology, Faculty of Medicine, Geneva University Hospitals, 1205 Geneva, Switzerland.
Ewout W SteyerbergJulius Center for Health Science and Primary Care, University Medical Center Utrecht, University of Utrecht, Utrecht 3508 GA, The Netherlands.
Jannick A N DorresteijnDepartment of Vascular Medicine, University Medical Center Utrecht, PO Box 85500, Utrecht 3508 GA, The Netherlands.
Emanuele Di AngelantonioCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.ORCID 0000-0001-8776-6719
Angela M WoodCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.ORCID 0000-0002-7937-304X
Frank L J VisserenDepartment of Vascular Medicine, University Medical Center Utrecht, PO Box 85500, Utrecht 3508 GA, The Netherlands.

Funding

BHF Chair Award CH/12/2/29428BHF-Turing Cardiovascular Data Science Award BCDSA\100005British Heart Foundation RG/18/13/33946British Heart Foundation RG/F/23/110103Cambridge BHF Centre of Research Excellence RE/18/1/34212Cambridge BHF Centre of Research Excellence RE/24/130011Dutch Heart Foundation 03-006-2023-0095Health Data Research UKJagiellonian University Medical College N43/DBS/000272NIHR NIHR303137NIHR Cambridge Biomedical Research Centre NIHR203312
6 · The paper itself

Abstract

aimsCurrent European Cardiovascular Disease (CVD) prevention guidelines recommend 10-year risk assessment using the SCORE2 model to identify individuals eligible for preventive treatment. Risk can be estimated using conventional risk charts or online calculators, though these methods may differ in precision and treatment classification. METHODS AND

resultsIndividuals without established CVD or diabetes mellitus were included from CPRD (UK, Europe's low risk region, n = 977 616) and HAPIEE (Czech Republic and Poland, high risk region and Lithuania, very high risk region, n = 11 739). During median 8.4 years [interquartile range (IQR) 5.0-10.4], 22 898 CVD events occurred. SCORE2 risk was estimated via two methods: an online calculator (unrounded SCORE2 algorithm) and risk charts from the 2021 ESC Prevention Guidelines. Predicted risks were higher with the risk charts than with the online calculator. In the low risk region, the median 10-year risk was 4.0% (IQR 2.0-6.0) with the risk charts vs. 3.7% (IQR 2.3-5.8) with the calculator. In the high/very high-risk region, risk was 9.0% (IQR 5.0-15.0) and 8.4% (IQR 4.5-13.9), respectively. Chart-based risk assessment resulted in higher treatment eligibility (6.3% vs. 4.0% in the low risk region; 51% vs. 43% in high/very high risk region). Discrimination was higher with the online calculator: difference in C-statistic +0.010 [95% confidence interval (CI) 0.008-0.012] in low risk region and +0.008 (95% CI 0.005-0.010) in high/very high risk region. Calibration was adequate for both approaches. Assuming a 50% relative risk reduction for preventive treatment, this corresponded to 53 vs. 46 events prevented per 1000 treated in the low-risk region and 80 vs. 74 in the high/very-high-risk region (calculator vs. risk charts).

conclusionRisk assessment using SCORE2 risk charts yields too high predicted risks and too broad treatment eligibility. By avoiding rounding of risk factors, the online calculator shows better discrimination.

Indexed as

Cardiovascular DiseasesAgedEuropeFemaleHumansMaleMiddle AgedRisk Assessment10-year CVD riskCardiovascular diseasePrimary preventionRisk prediction

Identifiers

PMID41071935
PMCPMC13016803

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.